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In this paper, an evolutionary hybrid approach is studied for fault diagnosis and it is applied to classify the loopers faults in hot rolling process. The algorithm called evolutionary KPCA-LSSVM is the combination of genetic algorithm (GA), kernel principal component analysis (KPCA) and Least Squares Support Vector Machine (LSSVM), which can obtain better fault recognition rate. Firstly, kernel function...
As the gene expression profiling data being with the characteristic of severe multicollinearity, small samples, and high dimension, it is difficult to build tumor classification model. Partial least square regression was applied as dimension reduction method to the model of tumor classification. Respectively, principal components are extracted from five gene expression profiling data sets: Gastric,...
This paper addressed multivariate calibration based on least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. LS-SVM technique have the advantages to provide the capability of learning a high dimensional feature with fewer training data, and to decrease the computational complexity for requiring only solving a set of linear equation...
The paper proposed a novel method for breast cancer detection using least square support vector machines. To overcome the high computational complexity of traditional support vector machines, recently a new technique, the least square SVM (LSSVM) has been introduced. In this method LSSVM simplifies the required computation to solving linear equation set. This equation set embodies all available information...
Atmospheric corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the feature selection of a small subset of several important environmental factors from many relevant ones. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing,...
In kernel based regression techniques (such as Support Vector Machines or Least Squares Support Vector Machines) it is hard to analyze the influence of perturbed inputs on the estimates. We show that for a nonlinear black box model a convex problem can be derived if it is linearized with respect to the influence of input perturbations. For this model an explicit prediction equation can be found. The...
Least squares support vector machine (LS-SVM) has been widely used in engineering practice, using for reference algorithm of combinatorial optimization, this paper puts forward the combinatorial optimization least squares support vector machine algorithm (COLS-SVM). Based on algorithmic analysis of COLS-SVM, it can be used on individual credit evaluation and compared with Lagrange support vector machine...
LS-SVM(least squares support vector machine) has been widely used in engineering practice. However, the solving of LS-SVM still remains difficult under the condition of large sample. Based on algorithm of combinatorial optimization, this paper put forward the combinatorial optimization least squares support vector machine algorithm. On several different data aggregation of dimensions, the numerical...
In this paper, we construct a novel control using LS-SVM matrix operator to achieve the stablization of wheeled under-actuated manipulators. Further, the relative degree of the regulated output is assumed to be known enabling the system is feedback linearizable. By Lyapunov's direct method, it is shown that the tracking error can be controlled in a small neighborhood of zero. The methodology is applicable...
The balance and motion control based on LS-SVM (least squares support vector machine) are considered for mobile wheeled inverted pendulums (WIP), in the presence of parametric and functional dynamics uncertainties. Based on Lyapunov synthesis, the proposed control mechanisms use the advantage of LS-SVM combined with on-line parameters estimation strategy in order to have an efficient approximation...
Method of support vector machine (SVM) as a new machine learning algorithm has shown its superiority of the ability of regression in the fields of damage identification. Through setting variation displacement of mode shape to the feature parameters of damage identification, the method of the damage identification of long-span cable-stayed bridge based on SVM is presented. The method of least square...
A solution to evaluate network workload by data fusion is put forward, which can be for surveillance the traffic of interconnected communications network in order to keep the network working well by identifying potentially serious problems in the early stages and evaluating network performance. Through fusing the historic network traffic data and network online traffic data, which is based on least...
Customer churns analysis and predication is an important part of customer relationship management (CRM). Because of the discrepancy of collecting channel and data gathering, raw customer data have imprecise, unbalanced and high dimensional characteristics, which degrade model performance. Customer retention and customer acquisition are two supports which have great influences on the bottom line compared...
It is very useful to predict the growth of internet user in China. A least square support vector machine regression model was introduced. Firstly, phase space reconstruction technology was used to deal with time series data, then with LSSVM, a method to predict the number of Internet user was constructed. The numerical experiments show that the accuracy of this prediction model has great advantage...
In this paper, we propose a new method for tumor classification using gene expression data. The new method expresses each testing sample as a linear combination of a set of metasamples extracted from all the training samples. Classification is achieved by a defined discriminating functions using the coefficient vector for the metasamples extracted from each category, which is obtained by l1-regularized...
By comparing and analysing the algorithm of least squares support vector machine (LS-SVM) based on Renyi-entropy, traditional least squares support vector machine and standard support vector machine(SVM), this paper concludes whether the number of training samples or training time, LS-SVM model based on Renyi-entropy are significantly better than the model of traditional LS-SVM and standard support...
As a new modeling thought, the accurate analytical redundancy model of power plant critical parameters was established by data mining method, which obtained effective information from the large number of real-time operation data. The basic modeling mode, including data preprocessing, mining model, verification model and the strategy from data to analytical redundancy model, was proposed in the paper...
In this paper, a novel learning method based on kernelized fuzzy clustering and least squares support vector machines (LSSVM) is presented to improve the generalization ability of a Takagi-Sugeno-Kang (TSK) fuzzy modeling. Firstly, the fuzzy partition of the product space of input and output is obtained by kernelized fuzzy clustering. Then, a computationally efficient numerical method is proposed...
It has been of great practical value to optimize sensors' locations and number for the self-diagnostic smart structures. Based on damage detection, Least Square Support Vector Machine (LS-SVM) is proposed to establish the performance function of damage detection for the piezoelectric smart structures, and then quantum genetic algorithm (QGA) is applied to optimize the performance function. To enhance...
The paper proposed a novel algorithm for texture classification system. This texture classification system is based on the extracted features on the performance of texture images' nonsubsampled contourlet transform (NSCT). To decrease the dimension of feature vector, we achieve the mean and standard deviation of NSCT coefficients matrix in different subbands and various directions. To compare the...
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